Failure Probability Modeling of Miniature DC Motors and Its Application in Fault Diagnosis | Zendy

Zhiping Xie | Zendy; Rongchen Zhao | Zendy; Jiming Zheng | Zendy; Yancheng Lang | Zendy

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Failure Probability Modeling of Miniature DC Motors and Its Application in Fault Diagnosis

Author(s) -

Zhiping Xie,

Rongchen Zhao,

Jiming Zheng,

Yancheng Lang

Publication year - 2021

Publication title -

shock and vibration

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.418

H-Index - 45

eISSN - 1875-9203

pISSN - 1070-9622

DOI - 10.1155/2021/9958412

Subject(s) - fault (geology) , process (computing) , reliability engineering , fault coverage , engineering , algorithm , computer science , pattern recognition (psychology) , control theory (sociology) , artificial intelligence , electrical engineering , electronic circuit , seismology , geology , operating system , control (management)

This paper proposes a fault diagnosis method for miniature DC motors (MDCMs) in the presence of the uncertainties caused by material and random factors of the production process. In this method, the probability models of fault multiple features are established based on the advantage criterion of the maximum overall average membership to determine the distribution of fault multiple features. The fault diagnosis algorithm is synthesized to obtain the threshold ranges of fault multiple features according to different confidence levels. Experimental test results are presented and analyzed to validate the efficiency and performance of the proposed fault diagnosis method.

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